P. Ošmera, B. Lacko, and M. Petr (Czech Republic)
Parallel Evolutionary Algorithms, Scheduling Problem
We are trying to piece together the knowledge of evolution with the help of biology, informatics and physics to create a complex evolutionary structure. It can speed up the creation of optimization algorithms with high quality features. The adaptive significance of GAs with sexual reproduction and an artificial immune system is presented. An artificial immune system was designed to support the parallel evolutionary algorithms. The majority of the research using evolutionary algorithms for the Scheduling Problem (SP) has only studied the static SP. Few evolutionary algorithms have been applied to the Dynamic Scheduling Problem (DSP). We implement hybrid and parallel genetic algorithms (GAs) for solving the dynamic SP. The adaptive significance of parallel GAs and the comparison with standard GAs are presented.
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